add dummy data and update default config and split ranges

dataset_infos.json

@@ -1 +1 @@
-{"mendeley": {"description": "\nElsevier OA CC-By is a corpus of 40k (40, 091) open access (OA) CC-BY articles\nfrom across Elsevier\u2019s journals and include the full text of the article, the metadata,\nthe bibliographic information for each reference, and author highlights.\n", "citation": "\n@article{Kershaw2020ElsevierOC,\n  title     = {Elsevier OA CC-By Corpus},\n  author    = {Daniel James Kershaw and R. Koeling},\n  journal   = {ArXiv},\n  year      = {2020},\n  volume    = {abs/2008.00774},\n  doi       = {https://doi.org/10.48550/arXiv.2008.00774},\n  url       = {https://elsevier.digitalcommonsdata.com/datasets/zm33cdndxs},\n  keywords  = {Science, Natural Language Processing, Machine Learning, Open Dataset},\n  abstract  = {We introduce the Elsevier OA CC-BY corpus. This is the first open\n               corpus of Scientific Research papers which has a representative sample\n               from across scientific disciplines. This corpus not only includes the\n               full text of the article, but also the metadata of the documents, \n               along with the bibliographic information for each reference.}\n}\n", "homepage": "https://elsevier.digitalcommonsdata.com/datasets/zm33cdndxs/3", "license": "CC-BY-4.0", "features": {"title": {"dtype": "string", "id": null, "_type": "Value"}, "abstract": {"dtype": "string", "id": null, "_type": "Value"}, "subjareas": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "keywords": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "asjc": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "body_text": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "author_highlights": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "elsevier_oa_cc_by", "config_name": "mendeley", "version": {"version_str": "1.0.0", "description": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 1080581428, "num_examples": 32072, "dataset_name": "elsevier_oa_cc_by"}, "test": {"name": "test", "num_bytes": 134576262, "num_examples": 4009, "dataset_name": "elsevier_oa_cc_by"}, "validation": {"name": "validation", "num_bytes": 134793940, "num_examples": 4008, "dataset_name": "elsevier_oa_cc_by"}}, "download_checksums": {"https://data.mendeley.com/public-files/datasets/zm33cdndxs/files/4e03ae48-04a7-44d4-b103-ce73e548679c/file_downloaded": {"num_bytes": 1008401964, "checksum": "885e5fedbc342a84ac3831396d8c057fcc9fe5abf318f13fb3303bdd8e2fac32"}}, "download_size": 1008401964, "post_processing_size": null, "dataset_size": 1349951630, "size_in_bytes": 2358353594}}
+{"mendeley": {"description": "\nElsevier OA CC-By is a corpus of 40k (40, 091) open access (OA) CC-BY articles\nfrom across Elsevier\u2019s journals and include the full text of the article, the metadata,\nthe bibliographic information for each reference, and author highlights.\n", "citation": "\n@article{Kershaw2020ElsevierOC,\n  title     = {Elsevier OA CC-By Corpus},\n  author    = {Daniel James Kershaw and R. Koeling},\n  journal   = {ArXiv},\n  year      = {2020},\n  volume    = {abs/2008.00774},\n  doi       = {https://doi.org/10.48550/arXiv.2008.00774},\n  url       = {https://elsevier.digitalcommonsdata.com/datasets/zm33cdndxs},\n  keywords  = {Science, Natural Language Processing, Machine Learning, Open Dataset},\n  abstract  = {We introduce the Elsevier OA CC-BY corpus. This is the first open\n               corpus of Scientific Research papers which has a representative sample\n               from across scientific disciplines. This corpus not only includes the\n               full text of the article, but also the metadata of the documents, \n               along with the bibliographic information for each reference.}\n}\n", "homepage": "https://elsevier.digitalcommonsdata.com/datasets/zm33cdndxs/3", "license": "CC-BY-4.0", "features": {"title": {"dtype": "string", "id": null, "_type": "Value"}, "abstract": {"dtype": "string", "id": null, "_type": "Value"}, "subjareas": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "keywords": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "asjc": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "body_text": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "author_highlights": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "elsevier_oa_cc_by", "config_name": "mendeley", "version": {"version_str": "1.0.0", "description": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 1080581428, "num_examples": 32072, "dataset_name": "elsevier_oa_cc_by"}, "test": {"name": "test", "num_bytes": 134576262, "num_examples": 4009, "dataset_name": "elsevier_oa_cc_by"}, "validation": {"name": "validation", "num_bytes": 134793940, "num_examples": 4008, "dataset_name": "elsevier_oa_cc_by"}}, "download_checksums": {"https://data.mendeley.com/public-files/datasets/zm33cdndxs/files/4e03ae48-04a7-44d4-b103-ce73e548679c/file_downloaded": {"num_bytes": 1008401964, "checksum": "885e5fedbc342a84ac3831396d8c057fcc9fe5abf318f13fb3303bdd8e2fac32"}}, "download_size": 1008401964, "post_processing_size": null, "dataset_size": 1349951630, "size_in_bytes": 2358353594}, "all": {"description": "\nElsevier OA CC-By is a corpus of 40k (40, 091) open access (OA) CC-BY articles\nfrom across Elsevier\u2019s journals and include the full text of the article, the metadata,\nthe bibliographic information for each reference, and author highlights.\n", "citation": "\n@article{Kershaw2020ElsevierOC,\n  title     = {Elsevier OA CC-By Corpus},\n  author    = {Daniel James Kershaw and R. Koeling},\n  journal   = {ArXiv},\n  year      = {2020},\n  volume    = {abs/2008.00774},\n  doi       = {https://doi.org/10.48550/arXiv.2008.00774},\n  url       = {https://elsevier.digitalcommonsdata.com/datasets/zm33cdndxs},\n  keywords  = {Science, Natural Language Processing, Machine Learning, Open Dataset},\n  abstract  = {We introduce the Elsevier OA CC-BY corpus. This is the first open\n               corpus of Scientific Research papers which has a representative sample\n               from across scientific disciplines. This corpus not only includes the\n               full text of the article, but also the metadata of the documents, \n               along with the bibliographic information for each reference.}\n}\n", "homepage": "https://elsevier.digitalcommonsdata.com/datasets/zm33cdndxs/3", "license": "CC-BY-4.0", "features": {"title": {"dtype": "string", "id": null, "_type": "Value"}, "abstract": {"dtype": "string", "id": null, "_type": "Value"}, "subjareas": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "keywords": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "asjc": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "body_text": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "author_highlights": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "elsevier_oa_cc_by", "config_name": "all", "version": {"version_str": "1.0.1", "description": null, "major": 1, "minor": 0, "patch": 1}, "splits": {"train": {"name": "train", "num_bytes": 1080581428, "num_examples": 32072, "dataset_name": "elsevier_oa_cc_by"}, "test": {"name": "test", "num_bytes": 134532311, "num_examples": 4008, "dataset_name": "elsevier_oa_cc_by"}, "validation": {"name": "validation", "num_bytes": 134823237, "num_examples": 4009, "dataset_name": "elsevier_oa_cc_by"}}, "download_checksums": {"https://data.mendeley.com/public-files/datasets/zm33cdndxs/files/4e03ae48-04a7-44d4-b103-ce73e548679c/file_downloaded": {"num_bytes": 1008401964, "checksum": "885e5fedbc342a84ac3831396d8c057fcc9fe5abf318f13fb3303bdd8e2fac32"}}, "download_size": 1008401964, "post_processing_size": null, "dataset_size": 1349936976, "size_in_bytes": 2358338940}}

dummy/all/1.0.1/dummy_data.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:bdabb82a40ce2702f719b6d3bf54b761483ee76f942fbc88754ab37a7ef3bb35
+size 13967789

elsevier-oa-cc-by.py

@@ -54,21 +54,19 @@ _HOMEPAGE = "https://elsevier.digitalcommonsdata.com/datasets/zm33cdndxs/3"
 
 _LICENSE = "CC-BY-4.0"
 
-_URLS = {
-    "mendeley": "https://data.mendeley.com/public-files/datasets/zm33cdndxs/files/4e03ae48-04a7-44d4-b103-ce73e548679c/file_downloaded"
-}
+_URL = "https://data.mendeley.com/public-files/datasets/zm33cdndxs/files/4e03ae48-04a7-44d4-b103-ce73e548679c/file_downloaded"
 
 
 class ElsevierOaCcBy(datasets.GeneratorBasedBuilder):
     """Elsevier OA CC-By Dataset."""
 
-    VERSION = datasets.Version("1.0.0")
+    VERSION = datasets.Version("1.0.1")
 
     BUILDER_CONFIGS = [
-        datasets.BuilderConfig(name="mendeley", version=VERSION, description="Official Mendeley dataset"),
+        datasets.BuilderConfig(name="all", version=VERSION, description="Official Mendeley dataset for Elsevier OA CC-By Corpus"),
     ]
 
-    DEFAULT_CONFIG_NAME = "mendeley"
+    DEFAULT_CONFIG_NAME = "all"
 
     def _info(self):
         features = datasets.Features(
@@ -105,11 +103,16 @@ class ElsevierOaCcBy(datasets.GeneratorBasedBuilder):
         # dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLS
         # It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files.
         # By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive
-        urls = _URLS[self.config.name]
-        data_dir = dl_manager.download_and_extract(urls)
+        data_dir = dl_manager.download_and_extract(_URL)
         
         corpus_path = os.path.join(data_dir, "json")
 
+        doc_count = len(glob.glob(f"{corpus_path}/*.json"))
+
+        train_split = [0, doc_count*80//100]
+        test_split = [doc_count*80//100+1, doc_count*90//100]
+        validation_split = [doc_count*90//100+1, doc_count]
+        
         return [
             datasets.SplitGenerator(
                 name=datasets.Split.TRAIN,
@@ -117,7 +120,7 @@ class ElsevierOaCcBy(datasets.GeneratorBasedBuilder):
                 gen_kwargs={
                     "filepath": corpus_path,
                     "split": "train",
-                    "split_range": [0, 32072]
+                    "split_range": train_split
                 },
             ),
             datasets.SplitGenerator(
@@ -126,7 +129,7 @@ class ElsevierOaCcBy(datasets.GeneratorBasedBuilder):
                 gen_kwargs={
                     "filepath": corpus_path,
                     "split": "test",
-                    "split_range": [32073, 36082]
+                    "split_range": test_split
                 },
             ),
             datasets.SplitGenerator(
@@ -135,14 +138,13 @@ class ElsevierOaCcBy(datasets.GeneratorBasedBuilder):
                 gen_kwargs={
                     "filepath": corpus_path,
                     "split": "validation",
-                    "split_range": [36083, 40091]
+                    "split_range": validation_split
                 },
             ),
         ]
 
     # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
     def _generate_examples(self, filepath, split, split_range):
-        # TODO: This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.
         # The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example.
         json_files = glob.glob(f"{filepath}/*.json")
         for doc in json_files[split_range[0]:split_range[1]]: